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基于植被动态的黄土高原生态地理分区
引用本文:张甜,彭建,刘焱序,赵明月.基于植被动态的黄土高原生态地理分区[J].地理研究,2015,34(9):1643-1661.
作者姓名:张甜  彭建  刘焱序  赵明月
作者单位:北京大学城市与环境学院,地表过程分析与模拟教育部重点实验室,北京 100871
基金项目:国家自然科学基金优秀青年科学基金项目(41322004)
摘    要:生态地理区划作为自然区划的新分支,近年来受到国内外地理学者的广泛关注,其在认识地理分异规律及区域规划活动中发挥着重要作用。传统生态地理分区多依据自上而下的三级演绎途径,且对于多分区方案的对比与优选缺乏定量化准则。黄土高原作为中国典型的生态脆弱区,植被生长与恢复对缓解当地生态困境十分重要,因此以植被多年动态一致性特征作为分区合理性的评价指标,有助于准确揭示当地生境特点及分异规律。为此,选取热量类、水分类、地形类及地表覆被类共9个指标,采用自组织映射网络(SOFM)与GIS空间分析技术,基于黄土高原近30年来自然本底与覆被状况进行生态地理分区;并依循植被动态一致性准则,依据两步筛选法对多种方案进行优选,最终将黄土高原分为六大生态地理区。研究表明:黄土高原修正6分区方案在12个备选分区方案中效果最好;同时,修正6分区方案多年平均NPP离散系数最低,表明该分区内部离散程度最小。分区方案与既有分区方案相比具有较好的一致性,但由于区划尺度存在一定的差异,整体区域划分更为清晰。对生态地理分区方案优选定量方法的探索,有助于提升自下而上生态地理区划的客观性。

关 键 词:生态地理分区  植被动态一致性  分区方案优选  SOFM神经网络  黄土高原  
收稿时间:2015-04-01
修稿时间:2015-07-08

Eco-geographical regionalization in Loess Plateau based on the dynamic consistency of vegetation
Tian ZHANG,Jian PENG,Yanxu LIU,Mingyue ZHAO.Eco-geographical regionalization in Loess Plateau based on the dynamic consistency of vegetation[J].Geographical Research,2015,34(9):1643-1661.
Authors:Tian ZHANG  Jian PENG  Yanxu LIU  Mingyue ZHAO
Institution:Laboratory for Earth Surface Process, Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Abstract:As a new branch of natural regionalization, eco-geographical regionalization is a core study subject of geography and ecology in recent years, and it has been widely concerned by scholars at home and abroad, which plays a very important role in understanding the geographical differentiation, social production and regional planning activities. However, the traditional researches on eco-geographical regionalization were mostly based on the three-level deduction method from top to bottom. Moreover, the existing researches of eco-geographical regionalization did not focus much on the optimization method on multi- regionalization. Meanwhile, the Loess Plateau in China was widely known as the typical ecological fragile zone, where the growth and restoration of vegetation are closely related with the mitigation of local ecological dilemma, therefore, it would be helpful to have a deeper recognition on the eco-environment of the Loess Plateau and its spatial distribution if we consider the condition of vegetation restoration as an important index to evaluate the rationality of regionalization. This paper selected the annual average temperature of January and July, the number of days with the temperature >10oC, annual precipitation, annual average solar radiation, the drought index, NDVI, DEM and vegetation coverage as the ecological indicators, and used a method based on self-organizing mapping neural network (SOFM) to evaluate the bioclimatic regionalization in the Loess Plateau. Then we discussed the spatial distribution of the chosen indicators based on the GIS spatial analysis and mapping function. In this paper, we compared the 12 types of regionalization in the Loess Plateau and chose the best one to reflect the vegetation restoration during 29 years in the study area based on the dynamic consistency of vegetation and the two-type screening method. Eventually, we found it more reasonable to divide the Loess Plateau into six parts, and each part could fundamentally fit the actual ecological condition and the spatial characteristics of the study area. At the same time, vegetation shows a similar growth trend in each part and the coefficient of the final regionalization scheme of variation index of the annual average NPP is the lowest, which means the aggregation degree of elements is the strongest inside the region. The regionalization scheme of this study has a good consistency with the existing regionalization scheme, and it is clearer than the existing ones because of the different regionalization scales. Therefore, this paper explored the multi-program optimization method in the eco-geographical regionalization, and enhanced the objectivity of the bottom-up eco-geographical regionalization.
Keywords:eco-geographical regionalization  consistency of vegetation dynamics  multi-program optimization  SOFM neural network  Loess Plateau  
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